Why does the MLP Classifier algorithm gives me some extreme values?

Question

I'm trying the MLP Classifier algorithm to give me insights into analyzing the past 666 results from drawing a single ball from 36 different urns (each urn has 10 balls, numbered 0 to 9) but when I asked the algorithm to give me the expected probabilities for each ball for the next drawing it comes up with extreme values. Here is the code:

import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler

# Assuming your data is stored in the 'results' list
# Each sublist represents the past results for an urn
results = [
    [3, 2, 7, ..., 6, 9],  # Urn 1
    [5, 2, ..., 0],       # Urn 2
    # ... (other urns)
    [4, 2, 7, ..., 1, 1]  # Urn 36
]

# Encode the past results as binary vectors
encoder = OneHotEncoder(categories=[range(10)] * 666, sparse=False)
X = np.vstack([encoder.fit_transform([urn]) for urn in results])

# Use the last ball drawn in each urn as the label
labels = np.array([urn[-1] for urn in results])

# Normalize the features
scaler = StandardScaler()
X_normalized = scaler.fit_transform(X)

# Initialize the MLP classifier with increased complexity
mlp = MLPClassifier(hidden_layer_sizes=(100, 100, 100), max_iter=1000, random_state=42)

# Train the model
mlp.fit(X_normalized, labels)

# Predict probabilities for each ball in the next drawing for all urns
for urn_idx, urn_results in enumerate(results):
    next_drawing_probs = mlp.predict_proba(scaler.transform(encoder.transform([urn_results])))
    print(f"Urn {urn_idx + 1} probabilities:")
    for ball, prob in enumerate(next_drawing_probs):
        print(f"  Ball {ball}: {prob:.4f}")


#And here is the output for the last 2 urns (which are like the output for all the other 34 urns):

Urn 35 probabilities:
  Ball 0: 0.0000
  Ball 1: 0.0000
  Ball 2: 0.0000
  Ball 3: 0.0000
  Ball 4: 0.0001
  Ball 5: 0.9999
  Ball 6: 0.0000
  Ball 7: 0.0000
  Ball 8: 0.0000
  Ball 9: 0.0000
Urn 36 probabilities:
  Ball 0: 0.0000
  Ball 1: 0.0000
  Ball 2: 0.0000
  Ball 3: 0.0000
  Ball 4: 0.0000
  Ball 5: 0.0000
  Ball 6: 0.0000
  Ball 7: 0.0000
  Ball 8: 0.0000
  Ball 9: 0.9999

Process finished with exit code 0

I have tried to increase the number of layers (up to 1000) but with the same results. I expect the probabilities for each ball to be closer to 0.1 (as low as 0.075 and as high as 0.125 perhaps)